DELAY-CFIM: A Sliding Window Based Method on Mining Closed Frequent Itemsets over High-Speed Data Streams
نویسندگان
چکیده
Closed frequent itemset mining plays an essential role in data stream mining. It could be used in business decisions, basket analysis, etc. Most methods for mining closed frequent itemsets store the streamlined information in compact data structure when data is generated. Whenever a query is submitted, it outputs all closed frequent itemsets. However, the online processing of existing approaches is so slow that those methods cannot deal with data streams generated at a high speed. In this paper, a novel method DELAY-CFIM for mining closed frequent itemsets is proposed to solve the problem of slow online processing. It divides the closed frequent itemset mining process over data streams into two steps. Firstly, when transactions are generated, it stores the frequency information of itemsets in a summary data structure. Then it mines closed frequent itemsets until a query is submitted. The method can improve the speed of online processing. Keywordsclosed frequent itemset, data stream, sliding window.
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